Conference Tourism: Exploring Economic Prospects in the Post-COVID-19 Era—Qualitative Research on Greek Hotel Executives
Bibliographic record
Abstract
As is widely known, the COVID-19 pandemic has affected tourism and related activities globally. Due to the restrictive measures implemented for gatherings and movements in order to limit the spread of the virus, conferences and conference tourism received a strong shock since the majority of them were canceled or postponed. At the end of the first quarter year, many countries, like Greece, started organizing digital and hybrid conferences. Therefore, there was a reset in the conference industry as the time when travel was limited allowed the organizers, as well as others involved, to work remotely. The present study aims to investigate the effects of the COVID-19 pandemic on conference tourism and, more specifically, how hotels and their conference facilities were affected. In addition, the pursuit of potential opportunities through the ‘New technologies’ adopted, as well as the shaping of the industry in the post-COVID-19 era, are studied. This is achieved through a qualitative methodology using semi-structured interviews with 27 executives of hotels that offer conference facilities in Athens, Thessaloniki, Heraklion and Rhodes in order to examine whether this specific sector has adapted to the new reality. The analysis of data in this form of research revealed that the pandemic had benefited conferences to some extent, but only under certain circumstances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".